There is a substantial body of research which has shown that quantitative information regarding tissue microstructure may be obtained from ultrasound backscatter data. These techniques typically employ analysis of ultrasound radiofrequency (RF) information, in one instance through spectroscopic analysis methods which have been shown to correlate to underlying features of tissue microstructure. The advent of a method of obtaining information regarding underlying tissue characteristics through ultrasound imaging has allowed for the branch of research known as ultrasound tissue characterization to materialize.
Some prior systems may refer to a limited degree to parallel processing to indicate processing which is conducted at the same time, in that it processes raw incoming ultrasound data in order to form an ultrasound data set from a multi-channel ultrasound imaging device, this in general refers to sequentially computing a set of aggregated values. This is because many ultrasound systems can have multiple channels of data being collected simultaneously which are typically processed independently in order to form the image (or beams), each of which are independently processed—or aggregated (e.g. an average intensity may be calculated for a given location) and then the aggregated values are independently and sequentially analyzed over a region of interest.
A theoretical framework behind ultrasound tissue characterization techniques was proposed by Lizzi and Feleppa [Lizzi F L, Greenebaum M, Feleppa E J, Elbaum M, Coleman D J. Theoretical framework for spectrum analysis in ultrasonic tissue characterization. Soc Am 1983; 73(4):1366-1373, Lizzi F L; Ostromogilsky M, Fellepa E J, Rork M C, Yaremko M M. Relationship of Ultrasonic Spectral Parameters to Features of Tissue Microstructure. IEEE Trans Ultrason Ferroelectr Freq Control 1986; 33:319-329;] Lizzi F L, Astor M, Liu T, Deng C, Coleman D J, Silverman R H. Ultrasonic spectrum analysis for tissue assays and therapy evaluation. Int J Imaging Syst Technol 1997; 8:3-10.]. They determined that characteristics in the frequency information of ultrasound backscatter data could be correlated to characteristics of sub-resolution ultrasound scatterers found in tissue. Initial research demonstrated the correlation between spectral characteristics of ultrasound RF data to features of retinal and liver tissue microstructures and has since broadened to encompass a variety of Quantitative ultrasound (QUS) analysis applications. QUS applications have been demonstrated to measure tissue micro-characteristics allowing for identification of various types and states [Czarnota G J, Kolios G J, Vaziri H. Ultrasound biomicroscopy of viable, dead and apoptotic cells. Ultrasound in Med and Biol. 1997; 23:961-965; Czarnota G J, Kolios M C, Abraham J. Ultrasound imaging of apoptosis: High-resolution noninvasive monitoring of programmed cell death in vitro, in situ and in vivo. Br J Cancer 1999; 81(3):520-527; Tunis A, Czarnota G J, Kolios M C. Monitoring structural changes in cells with high frequency ultrasound signal statistics. Ultrasound in Med and Biol. 2005; 31(8):1041-1049. Kolios M C, Czarnota G J, Hussain M, Foster F S, Hunt J W, Sherar M D. Analysis of ultrasound backscatter from ensembles of cells and isolated nuclei. IEEE Ultrasonics Symposium 2001; 2:1257-1260; Vlad R, Brand S, Kolios M C, Czarnota G J, Quantitative ultrasound characterization of response to radiotherapy in cancer mouse models. Clin Cancer Res 2009; 15:2067-2075; Vlad R, Brand S, Kolios M C, Czarnota G J. Quantitative ultrasound characterization of cancer radiotherapy effect in vivo. Int J Radiat Oncol Biol Phys 2008; 72:1236-1243; Banihashemi B, Vlad R, Debeljevic B, Giles A, Kolios M C, Czarnota G J. Ultrasound Imaging of Apoptosis in Tumor Response: Novel Preclinical Monitoring of Photodynamic Therapy Effects. Cancer Res 2008; 68:8590-8596; Sadeghi-Naini A, Papanicolau N, Falou O, Zubovits J, Dent R, Verma S, Trudeau M, Boileau J F, Spayne J, Iradji S, Sofroni E, Lee J, Lemon-Wong S, Yaffe M, Kolios M C, Czarnota G J. Quantitative Ultrasound Evaluation of Tumor Cell Death Response in Locally Advanced Breast Cancer Patients Receiving Chemotherapy. Clin Cancer Res. 2013; 19(8):2163-74; Sadeghi-Naini A, Falou O, Tadayyon H, Al-Mahrouki A, Tran W, Papanicolau N, Kolios M C, Czarnota G J. Conventional Frequency Ultrasonic Biomarkers of Cancer Treatment Response In Vivo. Transl Oncol. June 2013; 6(3): 234-243; Sadeghi-Naini A, Papanicolau N, Falou O, Tadayyon H, Lee H, Zubovits J, Sadeghian A, Karshafian R, Al-Mahrouki A, Giles A, Kolios M C, Czarnota G J. Low-frequency quantitative ultrasound imaging of cell death in vivo. Med. Phys. 40 (8), August 201; Sadeghi-Naini A, Falou O, Czarnota G J. Quantitative ultrasound visualization of cell death: Emerging clinical applications for detection of cancer treatment response. Conf Proc IEEE Eng Med Biol Soc. 2012; 2012:1125-8; Lakshmanan S, Tadayyon H, Sadeghi-Naini A, Falou O, Jahedmotlagh Z, Oelze M L, Czarnota G J. Evaluation of tumor cell death response in locally-advanced breast cancer patients to chemotherapy treatment by scattering property estimates using ultrasound backscatter. POMA 19, 075087 (2013); Taggart L R, Baddour R E, Giles A, Czarnota G J, Kolios M C. Ultrasonic characterization of whole cells and isolated nuclei. Ultrasound Med Biol. 2007; 33:389-401]. By providing the capacity to distinguish underlying cell morphologies, ultrasound tissue characterization techniques have been applied to a diverse set of fields such as distinguishing between areas of malignancy in the human prostate [E J Feleppa, A Kalisz, S Melgar, Typing of prostate tissue by ultrasonic spectrum analysis. IEEE Trans. Ultrason Ferroelec Freq Contr 1996: 43: pp 609-619; Ervis Sofroni, “Tissue Characterization of Prostate Cancer Using Quantitative Analysis of Low Frequency Ultrasound,” MSc, Computer Science, Ryerson Univeristy, Toronto, 2011] and differentiation of liver as well as cardiac abnormalities [R H Silverman, R Folberg, M J Rondeau, Spectral parameter imaging for detection of prognostically significant histologic features in uveal melanoma. Ultrasound in Med. and Biol. 2003: 29: pp 951-959; F L Lizzi, D L King, M C Rorke, Comparison of theoretical scattering results and ultrasonic data from clinical liver examinations. Ultrasound in Med. and Biol. 1988: 14: pp 377-385]. Further examples of QUS analysis techniques have been demonstrated to differentiate between benign fibroadenomas from mammary carcinomas and sarcomas, in the detection of apoptotic cell death leading to technologies for cancer treatment monitoring in a variety of experimental and clinical models, as well as many others.
Traditional approaches using standard central processing units (CPU) in general purpose computers are not well suited to solving these computational-intensive calculations efficiently across many locations points within a specimen. CPU architectures are designed for general purpose computing and not optimized for highly parallelizable data processing.
Currently, QUS parameters are being calculated using traditional computing methods which use the computers Central Processing Unit (CPU) to process ultrasound RF data. This approach to computing QUS parameters results in tens of seconds to minutes of processing time in order to obtain QUS parameters for a typical frame of ultrasound radiofrequency data on modern computing platforms. Although CPU technology has rapidly increased in processing capabilities, the resulting increase in QUS parameter calculations has not afforded the capability processing speeds approaching those required for real time data processing, particularly in view of the computational requirements for calculating QUS parameters of the higher resolution (or granularity) needed for some tissue characterization, coupled with demands for real-time or on-demand analysis or imaging.
The limitations of traditional CPU computing techniques to compute QUS parameters from ultrasound radiofrequency data has limited the use of the technology to a post processing paradigm. One drawback to standard CPU processing approaches is that they approach the analysis in a serial manner and do not have the processing capabilities necessary for real time requirements.
There is a need for a system which accelerates the processing throughput of QUS parameters, preferably for ultrasound tissue characterization.
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